Automated Detection of Hypertension Using Continuous Wavelet Transform and a Deep Neural Network with Ballistocardiography Signals
Article
Rajput, Jaypal Singh, Sharma, Manish, Kumar, T. Sudheer and Acharya, U. Rajendra. 2022. "Automated Detection of Hypertension Using Continuous Wavelet Transform and a Deep Neural Network with Ballistocardiography Signals." International Journal of Environmental Research and Public Health. 19 (7). https://doi.org/10.3390/ijerph19074014
| Article Title | Automated Detection of Hypertension Using Continuous Wavelet Transform and a Deep Neural Network with Ballistocardiography Signals |
|---|---|
| ERA Journal ID | 44293 |
| Article Category | Article |
| Authors | Rajput, Jaypal Singh, Sharma, Manish, Kumar, T. Sudheer and Acharya, U. Rajendra |
| Journal Title | International Journal of Environmental Research and Public Health |
| Journal Citation | 19 (7) |
| Article Number | 4014 |
| Number of Pages | 16 |
| Year | 2022 |
| Publisher | MDPI AG |
| Place of Publication | Switzerland |
| ISSN | 1660-4601 |
| 1661-7827 | |
| Digital Object Identifier (DOI) | https://doi.org/10.3390/ijerph19074014 |
| Web Address (URL) | https://www.mdpi.com/1660-4601/19/7/4014 |
| Abstract | Managing hypertension (HPT) remains a significant challenge for humanity. Despite advancements in blood pressure (BP)-measuring systems and the accessibility of effective and safe anti-hypertensive medicines, HPT is a major public health concern. Headaches, dizziness and fainting are common symptoms of HPT. In HPT patients, normalcy may be observed at one instant and abnormality may prevail during a long duration of 24 h ambulatory BP. This may cause difficulty in identifying patients with HPT, and hence there is a possibility that individuals may be untreated or administered insufficiently. Most importantly, uncontrolled HPT can lead to severe complications (stroke, heart attack, kidney disease, and heart failure), mainly ignoring the signs in nascent stages. HPT in the beginning stages may not present distinct symptoms and may be difficult to diagnose from standard physiological signals. Hence, ballistocardiography (BCG) signal was used in this study to detect HPT automatically. The processed signals from BCG were converted into scalogram images using a continuous wavelet transform (CWT) and were then fed into a 2-D convolutional neural network model (2D-CNN). The model was trained to learn and recognize BCG patterns of healthy controls (HC) and HPT classes. Our proposed model obtained a high classification accuracy of 86.14% with a ten-fold cross-validation (CV) strategy. Hence, this is the first use of a 2D-CNN model (deep-learning algorithm) to detect HPT employing BCG signals. |
| Keywords | BCG signal; hypertension; convolutional neural network; hypertension BCG signal classification; deep learning |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Byline Affiliations | Institute of Infrastructure, Technology, Research and Management (IITRAM), India |
| Ngee Ann Polytechnic, Singapore | |
| Asia University, Taiwan | |
| Singapore University of Social Sciences (SUSS), Singapore |
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